# Integrated method for chaotic time series analysis

Methods and apparatus for automatically detecting differences between similar but different states in a nonlinear process monitor nonlinear data. Steps include: acquiring the data; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis; obtaining time serial trends in the nonlinear measures; and determining by comparison whether differences between similar but different states are indicated.

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## Claims

1. A method for automatically discriminating between similar but different states in a nonlinear process comprising the steps of:

- (A) Operating a data provision means selected from the group consisting of data storage means and data acquisition means to provide at least one channel of nonlinear data, called e-data;
- (B) Separating the e-data into artifact data, called f-data, and artifact-free data, called g-data, while preventing phase distortions in the data;
- (C) Processing g-data through a filter to produce a filtered version of g-data, called h-data;
- (D) Applying at least one nonlinear measure to at least one type of data selected from the group consisting of e-data, f-data, g-data, and h-data to provide at least one time serial sequence of nonlinear measures from which at least one indicative trend selected from the group consisting of abrupt increases and abrupt decreases can be determined;
- (E) Comparing at least one indicative trend with at least one known discriminating indicator;
- (F) Determining from said comparison whether differences between similar but different states are indicated; and
- (G) Providing notification whether differences between similar but different states are indicated,

2. The method as described in claim 1 wherein said at least one time serial sequence of nonlinear measures is selected from the group consisting of: time per wave cycle for e-data, time per wave cycle for f-data, time per wave cycle for g-data, time per wave cycle for h-data, Kolmogorov entropy for e-data, Kolmogorov entropy for f-data, Kolmogorov entropy for g-data, Kolmogorov entropy for h-data, first minimum in the mutual information function for e-data, first minimum in the mutual information function for f-data, first minimum in the mutual information function for g-data, first minimum in the mutual information function for h-data, correlation dimension for e-data, correlation dimension for f-data, correlation dimension for g-data, correlation dimension for h-data, and combinations thereof.

3. The method as described in claim 1 wherein the e-data is separated into f-data and g-data by use of a zero-phase filter.

4. The method as described in claim 1 wherein the filter comprises a standard low-pass filter selected from the group consisting of second-order, third-order, and fourth-order low-pass filters at frequencies between about 35 Hz and about 60 Hz.

5. The method as described in claim 4 wherein the low-pass filter is a standard fourth-order low-pass filter at about 50 Hz.

6. Apparatus for automatically discriminating between similar but different states in a nonlinear process comprising:

- (A) Data provision means for providing at least one channel of nonlinear data, called e-data, said data provision means being selected from the group consisting of data storage means and data acquisition means;
- (B) Separation means for separating the e-data into artifact data, called f-data, and artifact-free data, called g-data, while preventing phase distortions in the data, communicably connected to said data provision means;
- (C) Filter means for filtering g-data to produce a filtered version of g-data, called h-data, communicably connected to said separation means;
- (D) Application means for applying at least one nonlinear measure to at least one type of data selected from the group consisting of e-data, f-data, g-data, and h-data to provide at least one time serial sequence of nonlinear measures, from which at least one indicative trend selected from the group consisting of abrupt increases and abrupt decreases can be determined, communicably connected to said filter means;
- (E) Comparison means for comparing at least one indicative trend with at least one known discriminating indicator, communicably connected to said application means;
- (F) Determination means for determining from said comparison whether differences between similar but different states are indicated, communicably connected to said comparison means; and
- (G) Notification means for providing notification whether differences between similar but different states are indicated, communicably connected to said determination means,

7. The apparatus as described in claim 6 wherein said at least one time serial sequence of nonlinear measures is selected from the group consisting of: time per wave cycle for e-data, time per wave cycle for f-data, time per wave cycle for g-data, time per wave cycle for h-data, Kolmogorov entropy for e-data, Kolmogorov entropy for f-data, Kolmogorov entropy for g-data, Kolmogorov entropy for h-data, first minimum in the mutual information function for e-data, first minimum in the mutual information function for f-data, first minimum in the mutual information function for g-data, first minimum in the mutual information function for h-data, correlation dimension for e-data, correlation dimension for f-data, correlation dimension for g-data, correlation dimension for h-data, and combinations thereof.

8. The apparatus as described in claim 6 wherein the e-data is separated into f-data and g-data by use of a zero-phase filter.

9. The apparatus as described in claim 8 wherein said filter means comprises a standard low-pass filter selected from the group consisting of second-order, third-order, and fourth-order low-pass filters at frequencies between about 35 Hz and about 60 Hz.

10. The apparatus as described in claim 9 wherein said low-pass filter is a standard fourth-order low-pass filter at about 50 Hz.

11. A method for automatically discriminating between similar but different states in a nonlinear process comprising the steps of:

- (A) Operating a data provision means selected from the group consisting of data storage means and data acquisition means to provide at least one channel of nonlinear data, called e-data;
- (B) Separating the e-data into artifact data, called f-data, and artifact-free data, called g-data, while preventing phase distortions in the data;
- (C) Processing g-data through a filter to produce a filtered version of g-data, called h-data;
- (D) Applying the lag derived from the first minimum in the mutual information function to create a d-dimensional probability density function which forms a high-dimensional topology for at least one type of data selected from the group consisting of e-data, f-data, g-data, and h-data whereby at least one indicative trend can be determined;
- (E) Comparing at least one indicative trend with at least one known discriminating indicator;
- (F) Determining from said comparison whether differences between similar but different states are indicated; and
- (G) Providing notification whether differences between similar but different states are indicated,

12. The method as described in claim 11 wherein the e-data is separated into f-data and g-data by use of a zero-phase filter.

13. The method as described in claim 12 wherein the filter comprises a standard low-pass filter selected from the group consisting of second-order, third-order, and fourth-order low-pass filters at frequencies between about 35 Hz and about 60 Hz.

14. The method as described in claim 13 wherein the low-pass filter is a standard fourth-order low-pass filter at about 50 Hz.

15. A method for automatically discriminating between similar but different states in a nonlinear process comprising the steps of:

- (A) Operating a data provision means selected from the group consisting of data storage means and data acquisition means to provide at least one channel of nonlinear data, called e-data;
- (B) Separating the e-data into artifact data, called f-data, and artifact-free data, called g-data, while preventing phase distortions in the data;
- (C) Processing g-data through a filter to produce a filtered version of g-data, called h-data;
- (D) Applying the lag derived from the first minimum in the mutual information function to create a d-dimensional probability density function which forms a high-dimensional topology for at least one type of data selected from the group consisting of e-data, f-data, g-data, and h-data whereby at least one indicative trend can be determined;
- (E) Generating a key from the set of indices of each bin in the multidimensional probability density function;
- (F) Representing occupied bins from the same class by a linked list to provide a reduction in the number of elements to be stored in the manner known as hashing whereby at least one hashed discriminating trend can be determined;
- (G) Comparing at least one indicative trend with at least one known discriminating indicator;
- (H) Determining from said comparison whether differences between similar but different states are indicated; and
- (I) Providing notification whether differences between similar but different states are indicated,

16. An apparatus for automatically discriminating between similar but different states in a nonlinear process comprising:

- (A) Data provision means for providing at least one channel of nonlinear data, called e-data, said data provision means being selected from the group consisting of data storage means and data acquisition means;
- (B) Separation means for separating the e-data into artifact data, called f-data, and artifact-free data, called g-data, while preventing phase distortions in the data, communicably connected to said data provision means;
- (C) Filter means for filtering g-data to produce a filtered version of g-data, called h-data, communicably connected to said separation means;
- (D) Application means for applying the lag derived from the first minimum in the mutual information function to create a d-dimensional probability density function which forms a high-dimensional topology for at least one type of data selected from the group consisting of e-data, f-data, g-data, and h-data whereby at least one indicative trend can be determined, communicably connected to said filter means;
- (E) Comparison means for comparing at least one indicative trend with at least one known discriminating indicator, communicably connected to said application means;
- (F) Determination means for determining from the comparison whether differences between similar but different states are indicated, communicably connected to said comparison means; and
- (G) Notification means for providing notification whether differences between similar but different states are indicated, communicably connected to said determination means,

17. The apparatus as described in claim 16 wherein the e-data is separated into f-data and g-data by use of a zero-phase filter.

18. The apparatus as described in claim 17 wherein the filter means comprises a standard low-pass filter selected from the group consisting of second-order, third-order, and fourth-order low-pass filters at frequencies between about 35 Hz and about 60 Hz.

19. The apparatus as described in claim 18 wherein the low-pass filter is a standard fourth-order low-pass filter at about 50 Hz.

20. An apparatus for automatically discriminating between similar but different states in a nonlinear process comprising:

- (A) Data provision means for providing at least one channel of nonlinear data, called e-data, said data provision means being selected from the group consisting of data storage means and data acquisition means;
- (B) Separation means for separating the e-data into artifact data, called f-data, and artifact-free data, called g-data, while preventing phase distortions in the data, communicably connected to said data provision means;
- (C) Filter means for filtering g-data to produce a filtered version of g-data, called h-data, communicably connected to said separation means;
- (D) Application means for applying the lag derived from the first minimum in the mutual information function to create a d-dimensional probability density function which forms a high-dimensional topology for at least one type of data selected from the group consisting of e-data, f-data, g-data, and h-data whereby at least one indicative trend can be determined, communicably connected to said filter means;
- (E) Generation means for generating a key from the set of indices of each bin in the multidimensional probability density function, communicably connected to said application means;
- (F) Representation means for representing occupied bins from the same class by a linked list to provide a reduction in the number of elements to be stored in the manner known as hashing, whereby at least one hashed indicative trend can be determined, communicably connected to said generation means;
- (G) Comparison means for comparing at least one indicative trend with at least one known discriminating indicator, communicably connected to said representation means;
- (H) Determination means for determining from said comparison whether differences between similar but different states are indicated, communicably connected to said comparison means; and
- (I) Notification means for providing notification whether differences between similar but different states are indicated, communicably connected to said determination means,

**Referenced Cited**

**U.S. Patent Documents**

5311876 | May 17, 1994 | Olsen et al. |

5349962 | September 27, 1994 | Lockard et al. |

5392788 | February 28, 1995 | Hudspeth |

5511537 | April 30, 1996 | Hively |

5626145 | May 6, 1997 | Clapp et al. |

**Other references**

- A.M. Fraser and H. L. Swinney, "Independent Coordinates for Strange Attractors from Mutual Information," Phys. Rev A 33, 1134-1140 (1986). R.C. Watt and S.R. Hameroff, "Phase Space Analysis of Human EEG during General Anesthesia," Ann. N.Y. Acad. Sci. 504, 286-288 (1987). G.A. Korn and T.M. Korn, Mathematical Handbook for Scientists and Engineers , McGraw Hill Book Company (second edition) 1968 (Section 19.6-19.7). M. Abramowitz and I. A. Stegun (ed.), Handbook of Mathematical Functions , U.S. Government Printing Office (Washington, D.C.) 1964 (Equation 26.4.11). H.D.I. Abarbanel, R. Brown, J.J. Sidorowich, and L. Sh. Tsimring, "The Analysis of Observed Chaotic Data in Physical Systems," Rev. Mod. Phys. 65, 1331-1392 (1993). P. Grassberger, T. Schreiber, C. Schaffrath, "Nonlinear Time Sequence Analysis," Int. J. Bifur. Chaos 3, 521-547 (1991). D.S. Broomhead and G.P. King, "Extracting Qualitative Dynamics from Experimental Data," Physica D 20, 217-236 (1986).

**Patent History**

**Patent number**: 5815413

**Type:**Grant

**Filed**: May 8, 1997

**Date of Patent**: Sep 29, 1998

**Assignee**: Lockheed Martin Energy Research Corporation (Oak Ridge, TN)

**Inventors**: Lee M. Hively (Philadelphia, TN), Esmond G. Ng (Concord, TN)

**Primary Examiner**: James P. Trammel

**Assistant Examiner**: Bryan Bui

**Attorney**: J. Kenneth Davis

**Application Number**: 8/853,226

**Classifications**

**Current U.S. Class**:

**364/574;**Signal Extraction Or Separation (e.g., Filtering) (702/190); For Noise Removal Or Suppression (702/191); Detecting Brain Electric Signal (600/544); With Feedback Of Signal To Patient (600/545)

**International Classification**: A61B 500;